Overview

Dataset statistics

Number of variables16
Number of observations46428
Missing cells18400
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 MiB
Average record size in memory158.8 B

Variable types

Numeric10
Categorical5
DateTime1

Alerts

name has a high cardinality: 45489 distinct valuesHigh cardinality
host_name has a high cardinality: 11081 distinct valuesHigh cardinality
neighbourhood has a high cardinality: 219 distinct valuesHigh cardinality
id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
price is highly overall correlated with room_typeHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
room_type is highly overall correlated with priceHigh correlation
last_review has 9182 (19.8%) missing valuesMissing
reviews_per_month has 9182 (19.8%) missing valuesMissing
minimum_nights is highly skewed (γ1 = 21.79076237)Skewed
name is uniformly distributedUniform
id has unique valuesUnique
number_of_reviews has 9182 (19.8%) zerosZeros
availability_365 has 17005 (36.6%) zerosZeros

Reproduction

Analysis started2023-07-09 18:06:23.972496
Analysis finished2023-07-09 18:06:38.264367
Duration14.29 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18918078
Minimum2539
Maximum36487245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:38.348703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1209898.9
Q19445461.2
median19544622
Q328937774
95-th percentile35225452
Maximum36487245
Range36484706
Interquartile range (IQR)19492312

Descriptive statistics

Standard deviation10931202
Coefficient of variation (CV)0.5778178
Kurtosis-1.2191019
Mean18918078
Median Absolute Deviation (MAD)9799792.5
Skewness-0.080743829
Sum8.7832853 × 1011
Variance1.1949118 × 1014
MonotonicityStrictly increasing
2023-07-09T23:36:38.481619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539 1
 
< 0.1%
25308563 1
 
< 0.1%
25309799 1
 
< 0.1%
25310242 1
 
< 0.1%
25310404 1
 
< 0.1%
25310497 1
 
< 0.1%
25311740 1
 
< 0.1%
25312773 1
 
< 0.1%
25313204 1
 
< 0.1%
25313748 1
 
< 0.1%
Other values (46418) 46418
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3647 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
ValueCountFrequency (%)
36487245 1
< 0.1%
36485609 1
< 0.1%
36485431 1
< 0.1%
36485057 1
< 0.1%
36484665 1
< 0.1%
36484363 1
< 0.1%
36484087 1
< 0.1%
36483152 1
< 0.1%
36483010 1
< 0.1%
36482809 1
< 0.1%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct45489
Distinct (%)98.0%
Missing15
Missing (%)< 0.1%
Memory size1.7 MiB
Hillside Hotel
 
18
Home away from home
 
17
New york Multi-unit building
 
13
Brooklyn Apartment
 
12
Loft Suite @ The Box House Hotel
 
11
Other values (45484)
46342 

Length

Max length179
Median length73
Mean length36.76735
Min length1

Characters and Unicode

Total characters1706483
Distinct characters768
Distinct categories20 ?
Distinct scripts11 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44878 ?
Unique (%)96.7%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowCozy Entire Floor of Brownstone
5th rowEntire Apt: Spacious Studio/Loft by central park

Common Values

ValueCountFrequency (%)
Hillside Hotel 18
 
< 0.1%
Home away from home 17
 
< 0.1%
New york Multi-unit building 13
 
< 0.1%
Brooklyn Apartment 12
 
< 0.1%
Loft Suite @ The Box House Hotel 11
 
< 0.1%
Private Room 11
 
< 0.1%
Private room 10
 
< 0.1%
Artsy Private BR in Fort Greene Cumberland 10
 
< 0.1%
Cozy Brooklyn Apartment 8
 
< 0.1%
Private room in Brooklyn 8
 
< 0.1%
Other values (45479) 46295
99.7%
(Missing) 15
 
< 0.1%

Length

2023-07-09T23:36:38.642637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 16203
 
5.7%
room 9976
 
3.5%
7815
 
2.8%
bedroom 7283
 
2.6%
private 7022
 
2.5%
apartment 6461
 
2.3%
cozy 4946
 
1.8%
apt 4410
 
1.6%
brooklyn 3918
 
1.4%
studio 3897
 
1.4%
Other values (11921) 210490
74.5%

Most occurring characters

ValueCountFrequency (%)
237569
 
13.9%
e 117726
 
6.9%
o 116911
 
6.9%
t 100052
 
5.9%
a 98692
 
5.8%
r 93300
 
5.5%
i 90377
 
5.3%
n 90086
 
5.3%
l 48969
 
2.9%
m 47334
 
2.8%
Other values (758) 665467
39.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1146598
67.2%
Uppercase Letter 251883
 
14.8%
Space Separator 237573
 
13.9%
Other Punctuation 31772
 
1.9%
Decimal Number 22880
 
1.3%
Dash Punctuation 6440
 
0.4%
Other Letter 2536
 
0.1%
Math Symbol 2412
 
0.1%
Close Punctuation 1470
 
0.1%
Open Punctuation 1331
 
0.1%
Other values (10) 1588
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
81
 
3.2%
46
 
1.8%
44
 
1.7%
41
 
1.6%
38
 
1.5%
36
 
1.4%
35
 
1.4%
35
 
1.4%
29
 
1.1%
29
 
1.1%
Other values (518) 2122
83.7%
Lowercase Letter
ValueCountFrequency (%)
e 117726
 
10.3%
o 116911
 
10.2%
t 100052
 
8.7%
a 98692
 
8.6%
r 93300
 
8.1%
i 90377
 
7.9%
n 90086
 
7.9%
l 48969
 
4.3%
m 47334
 
4.1%
s 45537
 
4.0%
Other values (57) 297614
26.0%
Other Symbol
ValueCountFrequency (%)
212
27.0%
155
19.8%
105
13.4%
37
 
4.7%
34
 
4.3%
30
 
3.8%
25
 
3.2%
15
 
1.9%
15
 
1.9%
14
 
1.8%
Other values (45) 142
18.1%
Uppercase Letter
ValueCountFrequency (%)
B 28028
 
11.1%
S 24719
 
9.8%
C 19963
 
7.9%
A 18361
 
7.3%
R 16833
 
6.7%
P 13884
 
5.5%
E 13057
 
5.2%
L 12914
 
5.1%
M 11116
 
4.4%
N 10812
 
4.3%
Other values (33) 82196
32.6%
Other Punctuation
ValueCountFrequency (%)
, 8742
27.5%
! 7439
23.4%
/ 4768
15.0%
. 4151
13.1%
& 3043
 
9.6%
' 1026
 
3.2%
* 835
 
2.6%
: 552
 
1.7%
# 501
 
1.6%
" 278
 
0.9%
Other values (11) 437
 
1.4%
Math Symbol
ValueCountFrequency (%)
+ 1223
50.7%
| 858
35.6%
~ 247
 
10.2%
= 32
 
1.3%
< 19
 
0.8%
> 19
 
0.8%
6
 
0.2%
4
 
0.2%
2
 
0.1%
× 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 8327
36.4%
2 6019
26.3%
3 2159
 
9.4%
5 1919
 
8.4%
0 1870
 
8.2%
4 1080
 
4.7%
6 503
 
2.2%
7 408
 
1.8%
8 366
 
1.6%
9 229
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 1416
96.3%
] 36
 
2.4%
8
 
0.5%
} 8
 
0.5%
2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1278
96.0%
[ 35
 
2.6%
8
 
0.6%
{ 8
 
0.6%
2
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 6374
99.0%
41
 
0.6%
24
 
0.4%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
21
56.8%
11
29.7%
5
 
13.5%
Modifier Symbol
ValueCountFrequency (%)
^ 9
56.2%
` 4
25.0%
´ 3
 
18.8%
Space Separator
ValueCountFrequency (%)
237569
> 99.9%
  4
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
188
83.6%
37
 
16.4%
Nonspacing Mark
ValueCountFrequency (%)
150
91.5%
14
 
8.5%
Connector Punctuation
ValueCountFrequency (%)
_ 42
97.7%
1
 
2.3%
Initial Punctuation
ValueCountFrequency (%)
37
82.2%
8
 
17.8%
Control
ValueCountFrequency (%)
181
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 86
100.0%
Other Number
ValueCountFrequency (%)
² 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1398278
81.9%
Common 305302
 
17.9%
Han 2226
 
0.1%
Cyrillic 191
 
< 0.1%
Inherited 164
 
< 0.1%
Katakana 136
 
< 0.1%
Hiragana 70
 
< 0.1%
Hangul 70
 
< 0.1%
Hebrew 31
 
< 0.1%
Georgian 13
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
81
 
3.6%
46
 
2.1%
44
 
2.0%
41
 
1.8%
38
 
1.7%
36
 
1.6%
35
 
1.6%
35
 
1.6%
29
 
1.3%
29
 
1.3%
Other values (399) 1812
81.4%
Common
ValueCountFrequency (%)
237569
77.8%
, 8742
 
2.9%
1 8327
 
2.7%
! 7439
 
2.4%
- 6374
 
2.1%
2 6019
 
2.0%
/ 4768
 
1.6%
. 4151
 
1.4%
& 3043
 
1.0%
3 2159
 
0.7%
Other values (118) 16711
 
5.5%
Latin
ValueCountFrequency (%)
e 117726
 
8.4%
o 116911
 
8.4%
t 100052
 
7.2%
a 98692
 
7.1%
r 93300
 
6.7%
i 90377
 
6.5%
n 90086
 
6.4%
l 48969
 
3.5%
m 47334
 
3.4%
s 45537
 
3.3%
Other values (67) 549294
39.3%
Hangul
ValueCountFrequency (%)
7
 
10.0%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (38) 43
61.4%
Cyrillic
ValueCountFrequency (%)
а 26
13.6%
о 18
 
9.4%
т 17
 
8.9%
н 15
 
7.9%
е 13
 
6.8%
р 11
 
5.8%
к 11
 
5.8%
м 10
 
5.2%
с 9
 
4.7%
в 9
 
4.7%
Other values (23) 52
27.2%
Katakana
ValueCountFrequency (%)
14
 
10.3%
12
 
8.8%
10
 
7.4%
9
 
6.6%
9
 
6.6%
9
 
6.6%
8
 
5.9%
7
 
5.1%
6
 
4.4%
6
 
4.4%
Other values (22) 46
33.8%
Hiragana
ValueCountFrequency (%)
16
22.9%
7
10.0%
7
10.0%
6
 
8.6%
5
 
7.1%
4
 
5.7%
4
 
5.7%
3
 
4.3%
2
 
2.9%
2
 
2.9%
Other values (13) 14
20.0%
Hebrew
ValueCountFrequency (%)
ו 5
16.1%
י 5
16.1%
ב 4
12.9%
ר 4
12.9%
ת 2
 
6.5%
ע 2
 
6.5%
ה 2
 
6.5%
ל 1
 
3.2%
ש 1
 
3.2%
ד 1
 
3.2%
Other values (4) 4
12.9%
Inherited
ValueCountFrequency (%)
150
91.5%
14
 
8.5%
Georgian
ValueCountFrequency (%)
13
100.0%
Devanagari
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1702145
99.7%
CJK 2226
 
0.1%
Misc Symbols 433
 
< 0.1%
None 423
 
< 0.1%
Punctuation 396
 
< 0.1%
Dingbats 297
 
< 0.1%
Cyrillic 191
 
< 0.1%
VS 164
 
< 0.1%
Hiragana 70
 
< 0.1%
Hangul 70
 
< 0.1%
Other values (7) 68
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
237569
 
14.0%
e 117726
 
6.9%
o 116911
 
6.9%
t 100052
 
5.9%
a 98692
 
5.8%
r 93300
 
5.5%
i 90377
 
5.3%
n 90086
 
5.3%
l 48969
 
2.9%
m 47334
 
2.8%
Other values (86) 661129
38.8%
Misc Symbols
ValueCountFrequency (%)
212
49.0%
105
24.2%
37
 
8.5%
15
 
3.5%
11
 
2.5%
6
 
1.4%
6
 
1.4%
6
 
1.4%
6
 
1.4%
4
 
0.9%
Other values (10) 25
 
5.8%
Punctuation
ValueCountFrequency (%)
188
47.5%
59
 
14.9%
41
 
10.4%
37
 
9.3%
37
 
9.3%
24
 
6.1%
8
 
2.0%
1
 
0.3%
1
 
0.3%
Dingbats
ValueCountFrequency (%)
155
52.2%
30
 
10.1%
25
 
8.4%
15
 
5.1%
14
 
4.7%
11
 
3.7%
8
 
2.7%
5
 
1.7%
5
 
1.7%
4
 
1.3%
Other values (11) 25
 
8.4%
VS
ValueCountFrequency (%)
150
91.5%
14
 
8.5%
CJK
ValueCountFrequency (%)
81
 
3.6%
46
 
2.1%
44
 
2.0%
41
 
1.8%
38
 
1.7%
36
 
1.6%
35
 
1.6%
35
 
1.6%
29
 
1.3%
29
 
1.3%
Other values (399) 1812
81.4%
None
ValueCountFrequency (%)
34
 
8.0%
à 27
 
6.4%
ó 24
 
5.7%
21
 
5.0%
15
 
3.5%
é 15
 
3.5%
14
 
3.3%
· 13
 
3.1%
12
 
2.8%
11
 
2.6%
Other values (69) 237
56.0%
Cyrillic
ValueCountFrequency (%)
а 26
13.6%
о 18
 
9.4%
т 17
 
8.9%
н 15
 
7.9%
е 13
 
6.8%
р 11
 
5.8%
к 11
 
5.8%
м 10
 
5.2%
с 9
 
4.7%
в 9
 
4.7%
Other values (23) 52
27.2%
Hiragana
ValueCountFrequency (%)
16
22.9%
7
10.0%
7
10.0%
6
 
8.6%
5
 
7.1%
4
 
5.7%
4
 
5.7%
3
 
4.3%
2
 
2.9%
2
 
2.9%
Other values (13) 14
20.0%
Georgian
ValueCountFrequency (%)
13
100.0%
Hangul
ValueCountFrequency (%)
7
 
10.0%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (38) 43
61.4%
Hebrew
ValueCountFrequency (%)
ו 5
16.1%
י 5
16.1%
ב 4
12.9%
ר 4
12.9%
ת 2
 
6.5%
ע 2
 
6.5%
ה 2
 
6.5%
ל 1
 
3.2%
ש 1
 
3.2%
ד 1
 
3.2%
Other values (4) 4
12.9%
Math Operators
ValueCountFrequency (%)
4
57.1%
2
28.6%
1
 
14.3%
Geometric Shapes
ValueCountFrequency (%)
4
36.4%
2
18.2%
2
18.2%
2
18.2%
1
 
9.1%
Devanagari
ValueCountFrequency (%)
2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Misc Technical
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

host_id
Real number (ℝ)

Distinct35770
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66451005
Minimum2438
Maximum2.7432131 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:38.786282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2438
5-th percentile807362.4
Q17719136.2
median30321518
Q31.0564047 × 108
95-th percentile2.3890395 × 108
Maximum2.7432131 × 108
Range2.7431888 × 108
Interquartile range (IQR)97921335

Descriptive statistics

Standard deviation77691273
Coefficient of variation (CV)1.1691512
Kurtosis0.26563043
Mean66451005
Median Absolute Deviation (MAD)27027080
Skewness1.2359882
Sum3.0851873 × 1012
Variance6.0359339 × 1015
MonotonicityNot monotonic
2023-07-09T23:36:38.908493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 272
 
0.6%
107434423 192
 
0.4%
137358866 103
 
0.2%
30283594 98
 
0.2%
12243051 95
 
0.2%
16098958 91
 
0.2%
61391963 91
 
0.2%
22541573 87
 
0.2%
7503643 52
 
0.1%
1475015 52
 
0.1%
Other values (35760) 45295
97.6%
ValueCountFrequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
ValueCountFrequency (%)
274321313 1
< 0.1%
274311461 1
< 0.1%
274307600 1
< 0.1%
274298453 1
< 0.1%
274273284 1
< 0.1%
274225617 1
< 0.1%
274195458 1
< 0.1%
274188386 1
< 0.1%
274103383 1
< 0.1%
274040642 1
< 0.1%

host_name
Categorical

Distinct11081
Distinct (%)23.9%
Missing21
Missing (%)< 0.1%
Memory size1.7 MiB
Michael
 
395
David
 
375
John
 
279
Sonder (NYC)
 
272
Alex
 
260
Other values (11076)
44826 

Length

Max length35
Median length31
Mean length6.1095955
Min length1

Characters and Unicode

Total characters283528
Distinct characters199
Distinct categories14 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6646 ?
Unique (%)14.3%

Sample

1st rowJohn
2nd rowJennifer
3rd rowElisabeth
4th rowLisaRoxanne
5th rowLaura

Common Values

ValueCountFrequency (%)
Michael 395
 
0.9%
David 375
 
0.8%
John 279
 
0.6%
Sonder (NYC) 272
 
0.6%
Alex 260
 
0.6%
Sarah 221
 
0.5%
Daniel 217
 
0.5%
Maria 199
 
0.4%
Blueground 192
 
0.4%
Jessica 188
 
0.4%
Other values (11071) 43809
94.4%

Length

2023-07-09T23:36:39.058001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1055
 
2.0%
and 589
 
1.1%
michael 438
 
0.8%
david 416
 
0.8%
sonder 367
 
0.7%
john 319
 
0.6%
alex 309
 
0.6%
laura 284
 
0.5%
nyc 282
 
0.5%
maria 234
 
0.5%
Other values (9966) 47391
91.7%

Most occurring characters

ValueCountFrequency (%)
a 36141
 
12.7%
e 27173
 
9.6%
i 23124
 
8.2%
n 22810
 
8.0%
r 16949
 
6.0%
l 14519
 
5.1%
o 12101
 
4.3%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.0%
Other values (189) 104508
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 223752
78.9%
Uppercase Letter 51840
 
18.3%
Space Separator 5372
 
1.9%
Other Punctuation 1509
 
0.5%
Open Punctuation 324
 
0.1%
Close Punctuation 322
 
0.1%
Dash Punctuation 198
 
0.1%
Other Letter 106
 
< 0.1%
Decimal Number 69
 
< 0.1%
Math Symbol 30
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
5.7%
5
 
4.7%
5
 
4.7%
5
 
4.7%
4
 
3.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
Other values (58) 68
64.2%
Lowercase Letter
ValueCountFrequency (%)
a 36141
16.2%
e 27173
12.1%
i 23124
10.3%
n 22810
10.2%
r 16949
 
7.6%
l 14519
 
6.5%
o 12101
 
5.4%
t 8937
 
4.0%
s 8673
 
3.9%
h 8593
 
3.8%
Other values (54) 44732
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 6068
11.7%
J 5146
 
9.9%
M 5070
 
9.8%
S 4477
 
8.6%
C 3531
 
6.8%
L 2760
 
5.3%
D 2616
 
5.0%
K 2511
 
4.8%
R 2374
 
4.6%
E 2270
 
4.4%
Other values (28) 15017
29.0%
Decimal Number
ValueCountFrequency (%)
5 16
23.2%
7 12
17.4%
0 11
15.9%
2 8
11.6%
4 7
10.1%
1 6
 
8.7%
6 3
 
4.3%
3 3
 
4.3%
8 2
 
2.9%
9 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
& 1097
72.7%
. 294
 
19.5%
/ 39
 
2.6%
, 35
 
2.3%
' 24
 
1.6%
@ 8
 
0.5%
" 6
 
0.4%
! 4
 
0.3%
: 2
 
0.1%
Space Separator
ValueCountFrequency (%)
5366
99.9%
6
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 324
100.0%
Close Punctuation
ValueCountFrequency (%)
) 322
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 198
100.0%
Math Symbol
ValueCountFrequency (%)
+ 30
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 275536
97.2%
Common 7830
 
2.8%
Han 89
 
< 0.1%
Cyrillic 56
 
< 0.1%
Hangul 9
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36141
 
13.1%
e 27173
 
9.9%
i 23124
 
8.4%
n 22810
 
8.3%
r 16949
 
6.2%
l 14519
 
5.3%
o 12101
 
4.4%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.1%
Other values (70) 96516
35.0%
Han
ValueCountFrequency (%)
6
 
6.7%
5
 
5.6%
5
 
5.6%
5
 
5.6%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
2
 
2.2%
2
 
2.2%
Other values (43) 51
57.3%
Common
ValueCountFrequency (%)
5366
68.5%
& 1097
 
14.0%
( 324
 
4.1%
) 322
 
4.1%
. 294
 
3.8%
- 198
 
2.5%
/ 39
 
0.5%
, 35
 
0.4%
+ 30
 
0.4%
' 24
 
0.3%
Other values (19) 101
 
1.3%
Cyrillic
ValueCountFrequency (%)
н 6
10.7%
а 6
10.7%
е 6
10.7%
л 4
 
7.1%
и 4
 
7.1%
А 4
 
7.1%
р 3
 
5.4%
й 3
 
5.4%
к 3
 
5.4%
с 3
 
5.4%
Other values (12) 14
25.0%
Hangul
ValueCountFrequency (%)
2
22.2%
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Hebrew
ValueCountFrequency (%)
ל 1
20.0%
א 1
20.0%
י 1
20.0%
נ 1
20.0%
ד 1
20.0%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283116
99.9%
None 240
 
0.1%
CJK 89
 
< 0.1%
Cyrillic 56
 
< 0.1%
Punctuation 10
 
< 0.1%
Hangul 9
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 36141
 
12.8%
e 27173
 
9.6%
i 23124
 
8.2%
n 22810
 
8.1%
r 16949
 
6.0%
l 14519
 
5.1%
o 12101
 
4.3%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.0%
Other values (67) 104096
36.8%
None
ValueCountFrequency (%)
é 104
43.3%
í 23
 
9.6%
á 20
 
8.3%
ú 19
 
7.9%
ë 13
 
5.4%
ô 11
 
4.6%
ó 9
 
3.8%
è 7
 
2.9%
ç 5
 
2.1%
ı 4
 
1.7%
Other values (19) 25
 
10.4%
CJK
ValueCountFrequency (%)
6
 
6.7%
5
 
5.6%
5
 
5.6%
5
 
5.6%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
2
 
2.2%
2
 
2.2%
Other values (43) 51
57.3%
Cyrillic
ValueCountFrequency (%)
н 6
10.7%
а 6
10.7%
е 6
10.7%
л 4
 
7.1%
и 4
 
7.1%
А 4
 
7.1%
р 3
 
5.4%
й 3
 
5.4%
к 3
 
5.4%
с 3
 
5.4%
Other values (12) 14
25.0%
Punctuation
ValueCountFrequency (%)
6
60.0%
2
 
20.0%
2
 
20.0%
Hangul
ValueCountFrequency (%)
2
22.2%
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Hebrew
ValueCountFrequency (%)
ל 1
20.0%
א 1
20.0%
י 1
20.0%
נ 1
20.0%
ד 1
20.0%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Manhattan
19855 
Brooklyn
19550 
Queens
5586 
Bronx
 
1072
Staten Island
 
365

Length

Max length13
Median length9
Mean length8.1570604
Min length5

Characters and Unicode

Total characters378716
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 19855
42.8%
Brooklyn 19550
42.1%
Queens 5586
 
12.0%
Bronx 1072
 
2.3%
Staten Island 365
 
0.8%

Length

2023-07-09T23:36:39.190206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T23:36:39.309792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 19855
42.4%
brooklyn 19550
41.8%
queens 5586
 
11.9%
bronx 1072
 
2.3%
staten 365
 
0.8%
island 365
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.4%
r 20622
 
5.4%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (10) 50742
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 331558
87.5%
Uppercase Letter 46793
 
12.4%
Space Separator 365
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 66648
20.1%
a 60295
18.2%
t 40440
12.2%
o 40172
12.1%
r 20622
 
6.2%
l 19915
 
6.0%
h 19855
 
6.0%
y 19550
 
5.9%
k 19550
 
5.9%
e 11537
 
3.5%
Other values (4) 12974
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
B 20622
44.1%
M 19855
42.4%
Q 5586
 
11.9%
S 365
 
0.8%
I 365
 
0.8%
Space Separator
ValueCountFrequency (%)
365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378351
99.9%
Common 365
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.5%
r 20622
 
5.5%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (9) 50377
13.3%
Common
ValueCountFrequency (%)
365
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.4%
r 20622
 
5.4%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (10) 50742
13.4%

neighbourhood
Categorical

Distinct219
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Williamsburg
3771 
Bedford-Stuyvesant
3647 
Harlem
 
2599
Bushwick
 
2442
Upper West Side
 
1814
Other values (214)
32155 

Length

Max length26
Median length17
Mean length11.925993
Min length4

Characters and Unicode

Total characters553700
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem

Common Values

ValueCountFrequency (%)
Williamsburg 3771
 
8.1%
Bedford-Stuyvesant 3647
 
7.9%
Harlem 2599
 
5.6%
Bushwick 2442
 
5.3%
Upper West Side 1814
 
3.9%
Hell's Kitchen 1769
 
3.8%
East Village 1737
 
3.7%
Upper East Side 1692
 
3.6%
Crown Heights 1528
 
3.3%
Midtown 1211
 
2.6%
Other values (209) 24218
52.2%

Length

2023-07-09T23:36:39.428969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 6298
 
8.4%
side 4376
 
5.8%
williamsburg 3771
 
5.0%
harlem 3693
 
4.9%
bedford-stuyvesant 3647
 
4.9%
upper 3506
 
4.7%
heights 3504
 
4.7%
village 2905
 
3.9%
west 2502
 
3.3%
bushwick 2442
 
3.3%
Other values (231) 38317
51.1%

Most occurring characters

ValueCountFrequency (%)
e 50730
 
9.2%
i 39813
 
7.2%
s 37999
 
6.9%
t 36585
 
6.6%
a 35885
 
6.5%
l 32481
 
5.9%
r 32219
 
5.8%
28533
 
5.2%
n 24857
 
4.5%
o 22938
 
4.1%
Other values (44) 211660
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 439490
79.4%
Uppercase Letter 79604
 
14.4%
Space Separator 28533
 
5.2%
Dash Punctuation 4172
 
0.8%
Other Punctuation 1901
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 50730
11.5%
i 39813
 
9.1%
s 37999
 
8.6%
t 36585
 
8.3%
a 35885
 
8.2%
l 32481
 
7.4%
r 32219
 
7.3%
n 24857
 
5.7%
o 22938
 
5.2%
d 18794
 
4.3%
Other values (15) 107189
24.4%
Uppercase Letter
ValueCountFrequency (%)
H 11337
14.2%
S 10953
13.8%
B 8190
10.3%
W 7758
9.7%
E 6784
8.5%
C 5040
 
6.3%
U 3567
 
4.5%
G 3554
 
4.5%
F 3112
 
3.9%
V 2947
 
3.7%
Other values (14) 16362
20.6%
Other Punctuation
ValueCountFrequency (%)
' 1778
93.5%
. 121
 
6.4%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
28533
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 519094
93.8%
Common 34606
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 50730
 
9.8%
i 39813
 
7.7%
s 37999
 
7.3%
t 36585
 
7.0%
a 35885
 
6.9%
l 32481
 
6.3%
r 32219
 
6.2%
n 24857
 
4.8%
o 22938
 
4.4%
d 18794
 
3.6%
Other values (39) 186793
36.0%
Common
ValueCountFrequency (%)
28533
82.5%
- 4172
 
12.1%
' 1778
 
5.1%
. 121
 
0.3%
, 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 553700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 50730
 
9.2%
i 39813
 
7.2%
s 37999
 
6.9%
t 36585
 
6.6%
a 35885
 
6.5%
l 32481
 
5.9%
r 32219
 
5.8%
28533
 
5.2%
n 24857
 
4.5%
o 22938
 
4.1%
Other values (44) 211660
38.2%

latitude
Real number (ℝ)

Distinct18791
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728572
Minimum40.49979
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:39.557702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.64538
Q140.68936
median40.72201
Q340.76333
95-th percentile40.826463
Maximum40.91306
Range0.41327
Interquartile range (IQR)0.07397

Descriptive statistics

Standard deviation0.055190472
Coefficient of variation (CV)0.00135508
Kurtosis0.099724991
Mean40.728572
Median Absolute Deviation (MAD)0.03641
Skewness0.25836604
Sum1890946.1
Variance0.0030459882
MonotonicityNot monotonic
2023-07-09T23:36:39.690821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 18
 
< 0.1%
40.68444 13
 
< 0.1%
40.68634 13
 
< 0.1%
40.69414 13
 
< 0.1%
40.71171 12
 
< 0.1%
40.68537 12
 
< 0.1%
40.71353 12
 
< 0.1%
40.7191 11
 
< 0.1%
40.69054 11
 
< 0.1%
40.71923 11
 
< 0.1%
Other values (18781) 46302
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50641 1
< 0.1%
40.50708 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.527 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91169 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90527 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%

longitude
Real number (ℝ)

Distinct14563
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.950968
Minimum-74.24442
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative46428
Negative (%)100.0%
Memory size1.7 MiB
2023-07-09T23:36:39.825182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-74.00326
Q1-73.9821
median-73.95457
Q3-73.934628
95-th percentile-73.86389
Maximum-73.71299
Range0.53143
Interquartile range (IQR)0.0474725

Descriptive statistics

Standard deviation0.046385832
Coefficient of variation (CV)-0.00062725119
Kurtosis4.9394791
Mean-73.950968
Median Absolute Deviation (MAD)0.02488
Skewness1.2497163
Sum-3433395.5
Variance0.0021516454
MonotonicityNot monotonic
2023-07-09T23:36:39.964935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95677 18
 
< 0.1%
-73.95427 17
 
< 0.1%
-73.95332 16
 
< 0.1%
-73.95136 16
 
< 0.1%
-73.9506 16
 
< 0.1%
-73.94791 16
 
< 0.1%
-73.95405 16
 
< 0.1%
-73.95725 15
 
< 0.1%
-73.98439 15
 
< 0.1%
-73.95742 15
 
< 0.1%
Other values (14553) 46268
99.7%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.7169 1
< 0.1%
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72247 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Entire home/apt
23252 
Private room
22036 
Shared room
 
1140

Length

Max length15
Median length15
Mean length13.477901
Min length11

Characters and Unicode

Total characters625752
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 23252
50.1%
Private room 22036
47.5%
Shared room 1140
 
2.5%

Length

2023-07-09T23:36:40.082035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T23:36:40.195947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
entire 23252
25.0%
home/apt 23252
25.0%
room 23176
25.0%
private 22036
23.7%
shared 1140
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 69680
11.1%
o 69604
11.1%
r 69604
11.1%
t 68540
11.0%
a 46428
 
7.4%
46428
 
7.4%
m 46428
 
7.4%
i 45288
 
7.2%
h 24392
 
3.9%
p 23252
 
3.7%
Other values (7) 116108
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 509644
81.4%
Space Separator 46428
 
7.4%
Uppercase Letter 46428
 
7.4%
Other Punctuation 23252
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 69680
13.7%
o 69604
13.7%
r 69604
13.7%
t 68540
13.4%
a 46428
9.1%
m 46428
9.1%
i 45288
8.9%
h 24392
 
4.8%
p 23252
 
4.6%
n 23252
 
4.6%
Other values (2) 23176
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
E 23252
50.1%
P 22036
47.5%
S 1140
 
2.5%
Space Separator
ValueCountFrequency (%)
46428
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 23252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 556072
88.9%
Common 69680
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 69680
12.5%
o 69604
12.5%
r 69604
12.5%
t 68540
12.3%
a 46428
8.3%
m 46428
8.3%
i 45288
8.1%
h 24392
 
4.4%
p 23252
 
4.2%
E 23252
 
4.2%
Other values (5) 69604
12.5%
Common
ValueCountFrequency (%)
46428
66.6%
/ 23252
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 625752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 69680
11.1%
o 69604
11.1%
r 69604
11.1%
t 68540
11.0%
a 46428
 
7.4%
46428
 
7.4%
m 46428
 
7.4%
i 45288
 
7.2%
h 24392
 
3.9%
p 23252
 
3.7%
Other values (7) 116108
18.6%

price
Real number (ℝ)

Distinct337
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.53802
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:40.305419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q165
median100
Q3160
95-th percentile275
Maximum350
Range340
Interquartile range (IQR)95

Descriptive statistics

Standard deviation71.862581
Coefficient of variation (CV)0.58645132
Kurtosis0.5184784
Mean122.53802
Median Absolute Deviation (MAD)44
Skewness1.0306644
Sum5689195
Variance5164.2306
MonotonicityNot monotonic
2023-07-09T23:36:40.434576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2051
 
4.4%
150 2047
 
4.4%
50 1534
 
3.3%
60 1458
 
3.1%
200 1401
 
3.0%
75 1370
 
3.0%
80 1272
 
2.7%
65 1190
 
2.6%
70 1170
 
2.5%
120 1130
 
2.4%
Other values (327) 31805
68.5%
ValueCountFrequency (%)
10 17
< 0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 1
 
< 0.1%
15 6
 
< 0.1%
16 6
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 33
0.1%
21 6
 
< 0.1%
ValueCountFrequency (%)
350 381
0.8%
349 45
 
0.1%
348 3
 
< 0.1%
347 4
 
< 0.1%
346 2
 
< 0.1%
345 25
 
0.1%
344 1
 
< 0.1%
343 4
 
< 0.1%
342 1
 
< 0.1%
341 4
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct107
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9431808
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:40.573381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation19.87751
Coefficient of variation (CV)2.8628823
Kurtosis873.93144
Mean6.9431808
Median Absolute Deviation (MAD)1
Skewness21.790762
Sum322358
Variance395.11539
MonotonicityNot monotonic
2023-07-09T23:36:40.712235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12148
26.2%
2 11199
24.1%
3 7506
16.2%
30 3534
 
7.6%
4 3106
 
6.7%
5 2854
 
6.1%
7 1975
 
4.3%
6 694
 
1.5%
14 543
 
1.2%
10 464
 
1.0%
Other values (97) 2405
 
5.2%
ValueCountFrequency (%)
1 12148
26.2%
2 11199
24.1%
3 7506
16.2%
4 3106
 
6.7%
5 2854
 
6.1%
6 694
 
1.5%
7 1975
 
4.3%
8 129
 
0.3%
9 79
 
0.2%
10 464
 
1.0%
ValueCountFrequency (%)
1250 1
 
< 0.1%
999 3
 
< 0.1%
500 5
 
< 0.1%
480 1
 
< 0.1%
400 1
 
< 0.1%
370 1
 
< 0.1%
366 1
 
< 0.1%
365 24
0.1%
364 1
 
< 0.1%
360 5
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct393
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.827712
Minimum0
Maximum629
Zeros9182
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:40.845898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile116
Maximum629
Range629
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.190521
Coefficient of variation (CV)1.8965531
Kurtosis18.944347
Mean23.827712
Median Absolute Deviation (MAD)5
Skewness3.64035
Sum1106273
Variance2042.1832
MonotonicityNot monotonic
2023-07-09T23:36:40.975266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9182
19.8%
1 4976
 
10.7%
2 3318
 
7.1%
3 2390
 
5.1%
4 1918
 
4.1%
5 1526
 
3.3%
6 1297
 
2.8%
7 1130
 
2.4%
8 1080
 
2.3%
9 924
 
2.0%
Other values (383) 18687
40.2%
ValueCountFrequency (%)
0 9182
19.8%
1 4976
10.7%
2 3318
 
7.1%
3 2390
 
5.1%
4 1918
 
4.1%
5 1526
 
3.3%
6 1297
 
2.8%
7 1130
 
2.4%
8 1080
 
2.3%
9 924
 
2.0%
ValueCountFrequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%
Distinct1754
Distinct (%)4.7%
Missing9182
Missing (%)19.8%
Memory size1.7 MiB
Minimum2011-03-28 00:00:00
Maximum2019-07-08 00:00:00
2023-07-09T23:36:41.123641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:41.262506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct936
Distinct (%)2.5%
Missing9182
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean1.377473
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:41.396206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.715
Q32.02
95-th percentile4.67
Maximum58.5
Range58.49
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.6904934
Coefficient of variation (CV)1.2272425
Kurtosis43.099274
Mean1.377473
Median Absolute Deviation (MAD)0.615
Skewness3.1549195
Sum51305.36
Variance2.8577679
MonotonicityNot monotonic
2023-07-09T23:36:41.524462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 886
 
1.9%
0.05 856
 
1.8%
1 828
 
1.8%
0.03 772
 
1.7%
0.04 639
 
1.4%
0.16 638
 
1.4%
0.08 580
 
1.2%
0.09 564
 
1.2%
0.06 557
 
1.2%
0.11 527
 
1.1%
Other values (926) 30399
65.5%
(Missing) 9182
 
19.8%
ValueCountFrequency (%)
0.01 40
 
0.1%
0.02 886
1.9%
0.03 772
1.7%
0.04 639
1.4%
0.05 856
1.8%
0.06 557
1.2%
0.07 453
1.0%
0.08 580
1.2%
0.09 564
1.2%
0.1 438
0.9%
ValueCountFrequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%
Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6725037
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:41.665423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile13
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation31.083436
Coefficient of variation (CV)4.6584368
Kurtosis75.60396
Mean6.6725037
Median Absolute Deviation (MAD)0
Skewness8.3505074
Sum309791
Variance966.18001
MonotonicityNot monotonic
2023-07-09T23:36:41.790096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 30677
66.1%
2 6436
 
13.9%
3 2745
 
5.9%
4 1354
 
2.9%
5 808
 
1.7%
6 529
 
1.1%
8 396
 
0.9%
7 390
 
0.8%
327 272
 
0.6%
9 225
 
0.5%
Other values (37) 2596
 
5.6%
ValueCountFrequency (%)
1 30677
66.1%
2 6436
 
13.9%
3 2745
 
5.9%
4 1354
 
2.9%
5 808
 
1.7%
6 529
 
1.1%
7 390
 
0.8%
8 396
 
0.9%
9 225
 
0.5%
10 203
 
0.4%
ValueCountFrequency (%)
327 272
0.6%
232 192
0.4%
121 98
 
0.2%
103 103
 
0.2%
96 186
0.4%
91 91
 
0.2%
87 87
 
0.2%
65 50
 
0.1%
52 104
 
0.2%
50 49
 
0.1%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.67685
Minimum0
Maximum365
Zeros17005
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-09T23:36:41.921752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q3217
95-th percentile358
Maximum365
Range365
Interquartile range (IQR)217

Descriptive statistics

Standard deviation130.41395
Coefficient of variation (CV)1.1890745
Kurtosis-0.92278392
Mean109.67685
Median Absolute Deviation (MAD)40
Skewness0.80642813
Sum5092077
Variance17007.799
MonotonicityNot monotonic
2023-07-09T23:36:42.054373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17005
36.6%
365 1122
 
2.4%
364 430
 
0.9%
1 397
 
0.9%
89 334
 
0.7%
5 333
 
0.7%
3 296
 
0.6%
179 273
 
0.6%
90 270
 
0.6%
2 254
 
0.5%
Other values (356) 25714
55.4%
ValueCountFrequency (%)
0 17005
36.6%
1 397
 
0.9%
2 254
 
0.5%
3 296
 
0.6%
4 227
 
0.5%
5 333
 
0.7%
6 240
 
0.5%
7 212
 
0.5%
8 228
 
0.5%
9 187
 
0.4%
ValueCountFrequency (%)
365 1122
2.4%
364 430
 
0.9%
363 215
 
0.5%
362 150
 
0.3%
361 101
 
0.2%
360 95
 
0.2%
359 127
 
0.3%
358 160
 
0.3%
357 83
 
0.2%
356 73
 
0.2%

Interactions

2023-07-09T23:36:35.688838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:25.823924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.916765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.985672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.046501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.208611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.299639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.388503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.479711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.583103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.807278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:25.933575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.028058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.095304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.159171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.323396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.413341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.504792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.590754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.696144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.913949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.037870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.129509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.195938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.264575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.429741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.518722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.610374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.696962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.803718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.018855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.141342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.231397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.291388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.370156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.530850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.620339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.717425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.816504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.905504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.133345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.250212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.337160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.394331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.482242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.640595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.726592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.824968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.922141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.015365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.252631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.364369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.447227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.504001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.606249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.749358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.838195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.933558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.034713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.127345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.363396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.475073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.551816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.607868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.719261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.858513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.943812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.042899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.145466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.237043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.477134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.583593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.659769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.715370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.838459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.967308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.053743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.148421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.254649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.346313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.590734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.692799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.767532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.832283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:29.961823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.078959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.162342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.258112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.362108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.460865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:36.703512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:26.803556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:27.876373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:28.938833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:30.081516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:31.190196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:32.275845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:33.366604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:34.469987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-09T23:36:35.571499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-09T23:36:42.174956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhood_grouproom_type
id1.0000.552-0.0010.084-0.041-0.061-0.3060.3550.1350.1580.0650.071
host_id0.5521.0000.0440.125-0.100-0.136-0.1220.2620.1450.1660.1030.098
latitude-0.0010.0441.0000.0430.1300.020-0.039-0.026-0.000-0.0150.5390.109
longitude0.0840.1250.0431.000-0.423-0.1190.0720.1290.0680.0890.6520.146
price-0.041-0.1000.130-0.4231.0000.103-0.026-0.020-0.1230.0540.1810.503
minimum_nights-0.061-0.1360.020-0.1190.1031.000-0.180-0.2930.0640.0740.0050.014
number_of_reviews-0.306-0.122-0.0390.072-0.026-0.1801.0000.7140.0650.2530.0260.022
reviews_per_month0.3550.262-0.0260.129-0.020-0.2930.7141.0000.1550.4010.0480.028
calculated_host_listings_count0.1350.145-0.0000.068-0.1230.0640.0650.1551.0000.4110.0880.097
availability_3650.1580.166-0.0150.0890.0540.0740.2530.4010.4111.0000.0860.093
neighbourhood_group0.0650.1030.5390.6520.1810.0050.0260.0480.0880.0861.0000.115
room_type0.0710.0980.1090.1460.5030.0140.0220.0280.0970.0930.1151.000

Missing values

2023-07-09T23:36:37.618959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-09T23:36:37.922619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-09T23:36:38.152300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02539Clean & quiet apt home by the park2787JohnBrooklynKensington40.64749-73.97237Private room149192018-10-190.216365
12595Skylit Midtown Castle2845JenniferManhattanMidtown40.75362-73.98377Entire home/apt2251452019-05-210.382355
23647THE VILLAGE OF HARLEM....NEW YORK !4632ElisabethManhattanHarlem40.80902-73.94190Private room15030NaTNaN1365
33831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton Hill40.68514-73.95976Entire home/apt8912702019-07-054.641194
45022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast Harlem40.79851-73.94399Entire home/apt801092018-11-190.1010
55099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray Hill40.74767-73.97500Entire home/apt2003742019-06-220.591129
65121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.68688-73.95596Private room6045492017-10-050.4010
75178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76489-73.98493Private room7924302019-06-243.471220
85203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80178-73.96723Private room7921182017-07-210.9910
95238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71344-73.99037Entire home/apt15011602019-06-091.334188
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
4888536482809Stunning Bedroom NYC! Walking to Central Park!!131529729KendallManhattanEast Harlem40.79633-73.93605Private room7520NaTNaN2353
4888636483010Comfy 1 Bedroom in Midtown East274311461ScottManhattanMidtown40.75561-73.96723Entire home/apt20060NaTNaN1176
4888736483152Garden Jewel Apartment in Williamsburg New York208514239MelkiBrooklynWilliamsburg40.71232-73.94220Entire home/apt17010NaTNaN3365
4888836484087Spacious Room w/ Private Rooftop, Central location274321313KatManhattanHell's Kitchen40.76392-73.99183Private room12540NaTNaN131
4888936484363QUIT PRIVATE HOUSE107716952MichaelQueensJamaica40.69137-73.80844Private room6510NaTNaN2163
4889036484665Charming one bedroom - newly renovated rowhouse8232441SabrinaBrooklynBedford-Stuyvesant40.67853-73.94995Private room7020NaTNaN29
4889136485057Affordable room in Bushwick/East Williamsburg6570630MarisolBrooklynBushwick40.70184-73.93317Private room4040NaTNaN236
4889236485431Sunny Studio at Historical Neighborhood23492952Ilgar & AyselManhattanHarlem40.81475-73.94867Entire home/apt115100NaTNaN127
488933648560943rd St. Time Square-cozy single bed30985759TazManhattanHell's Kitchen40.75751-73.99112Shared room5510NaTNaN62
4889436487245Trendy duplex in the very heart of Hell's Kitchen68119814ChristopheManhattanHell's Kitchen40.76404-73.98933Private room9070NaTNaN123